DocumentCode :
3777259
Title :
Improved hybrid genetic algorithm for Job Shop problem
Author :
Ming Huang; GuoLi Cheng; Xu Liang
Author_Institution :
Software College, Dalian Jiaotong University, 116028, China
Volume :
1
fYear :
2015
Firstpage :
249
Lastpage :
253
Abstract :
Job shop scheduling problem, due to its discrete, dynamic, multi-machine, multi-variables, constraining and other typical NP-hard resistance natures, is bound to play an important role in NP studies. This paper proposes a new improved genetic algorithm, the isolation niche algorithm and adaptive genetic algorithm, for the sake of improving quality of solutions. Based on numerous examples and data analysis, it is rather safe to conclude that the proposed hybrid genetic algorithm has significant advantages.
Keywords :
"Genetic algorithms","Job shop scheduling","Sociology","Statistics","Algorithm design and analysis","Optimization"
Publisher :
ieee
Conference_Titel :
Computer Science and Network Technology (ICCSNT), 2015 4th International Conference on
Type :
conf
DOI :
10.1109/ICCSNT.2015.7490746
Filename :
7490746
Link To Document :
بازگشت